Industrial Composting Bioreactor

Tune carbon-to-nitrogen ratio, moisture and aeration in an industrial composting bioreactor to maximise heat, biogas and finished compost yield.

▶ Open the simulation

Why this matters

Tune carbon-to-nitrogen ratio, moisture and aeration in an industrial composting bioreactor to maximise heat, biogas and finished compost yield.

This model is adapted from an internal scenario-planning tool, distilled here into three linked calculations that mirror how real operators, engineers and analysts reason about the system.

How the model works

Carbon:Nitrogen ratio (:1) — C:N balance score — thermophilic composting bacteria thrive closest to a 30:1 ratio.
Core temperature (°C) — Thermal removal rate at a fixed 420 m³/hr airflow — too hot needs cooling, too cold stalls decomposition.
Retention time (days) — Fraction of a 48-tonne feedstock batch converted to finished compost at 2.8%/day decomposition rate.

Reading the results

Each control drives one of three underlying formulas taken from the source engineering model. Moving a slider recomputes its metric instantly and updates the 3D bar in the simulation — taller, brighter bars mean the system is closer to its optimum operating envelope. Try pushing each parameter to its extreme to see where the model breaks down or saturates.

Frequently Asked Questions

What is industrial composting bioreactor used for?

Tune carbon-to-nitrogen ratio, moisture and aeration in an industrial composting bioreactor to maximise heat, biogas and finished compost yield.

Is this a real-world engineering model or a toy?

The underlying formulas are simplified versions of real planning heuristics used in this domain — accurate enough to show the right trends and trade-offs, but not a substitute for full engineering simulation software.

Can I use my own numbers?

Yes — every slider in the simulation maps directly onto one of the model's input variables, so you can explore scenarios well outside the defaults shown here.

Why does the bar height saturate at the extremes?

Each metric is normalised to a 0–1 range against a realistic reference ceiling from the source model, so very large inputs will visually cap out even though the underlying number keeps growing.